Edge Federation: Towards an Integrated Service Provisioning Model

Cao, Xiaofeng; Tang, Guoming; Guo, Deke; Li, Yan; Zhang, Weiming

doi:10.1109/tnet.2020.2979361

Cited by 82 publications

(45 citation statements)

References 28 publications

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“…Hence, enabling cooperative edge computing environment can open the resource of many types of edge computing servers for serving the diverse requirements in the dense IoT networks. However, to realize the cooperation among the edge nodes to maximize their benefits, several particular challenges should be solved: The trade-off between the cloud and the edge; The optimization of the service placement on distributed and limited edge resources; The contradiction between the computation-intensive edge services and the limited edge resources [201].…”

Section: Learned Lessons and Potential Workmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: Learned Lessons and Potential Workmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…Our work in this paper differs substantially from the previous works [29], [30], which focused on workload co-location in cloud environment. To further improve edge resources, a resource management scheme which seamlessly integrates or federates resources across multiple edge, such that the resources are holistically managed has been proposed in [18], [19], [25], [26]. Our recent work [19] considered a dependency-aware task dispatching and colocation in a federated edge system.…”

Section: Related Workmentioning

confidence: 99%

“…Aerial edge federation makes it easier to manage multiple drone deployments by synchronizing resources across multiple drones, enabling flexible tasks execution and preventing lockin situations. Having a federated edge minimizes latency by serving users from the edge that is the closest to them [18], [19]. A recent lightweight Kubernetes-based edge tool, called Kubermatic 1 , can deploy and manage multiple edge resources running across multi-drones with a single management interface, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

Awada

Zhang

Chen

et al. 2022

IEEE Trans. Veh. Technol.

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“…Constraint condition (26) ensures that each container is hosted on one edge node at most. Limitation condition (27) assures that containers will not be allocated on edge nodes turned off. Equation ( 28) defines the value of variable η i as the ratio of the sum of containers distributed to the maximum number of containers that can host on edge node i.…”

Section: Optimal Coalition Allocation Utilitymentioning

confidence: 99%

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

Wang

Dang

Zhao

et al. 2021

Preprint

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The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale IoT services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. The coalition method has been used in MEC for resource optimization, latency, energy consumption reduction, computation offloading, etc. However, the majority of research does not consider the price of computing resources corresponded to a container. Moreover, each SP does not choose EIP with the highest cost-performance to sign a medium/long-term computing resource purchase or lease contract. In this work, we consider a scenario with a collection of SPs with different budgets and several EIPs distributed in geographical locations. During the first phase, we get the market equilibrium price and select the optimal EIPs to make a deal by solving the Eisenberg-Gale convex program. In the second stage, using a mathematical model, we maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that the effectiveness of our method is significantly better than the existing model.

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Edge Federation: Towards an Integrated Service Provisioning Model

Cited by 82 publications

References 28 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

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